摘要
Selecting the minimum number of linear models required to characterize a nonlinear dynamic system in the local approach to linear parameter varying(LPV)model identification remains a challenge.This study proposes the use of the gap metric for such selection.Subsequently,the identified LPV model is proposed for use in the design of LPV-based model predictive control(MPC).The results of the validation experiment reveal that the identified gap-based local and global LPV models provide a good fit,which is superior to that of any individual linear model,as measured by the computed mean squared error values.Furthermore,the closed-loop results show that the performance of the LPV-MPC closely matches that of the full-blown nonlinear MPC and multi-MPC,while LPV-MPC clearly outperforms Linear MPC.Additionally,the proposed LPV-MPC is found to be robust to process model mismatches and measurement noise,and is much less computationally intensive than nonlinear MPC.
